Unsupervised training method, training apparatus, and training program for an N-gram language model based upon recognition reliability

ABSTRACT

A computer-based, unsupervised training method for an N-gram language model includes reading, by a computer, recognition results obtained as a result of speech recognition of speech data; acquiring, by the computer, a reliability for each of the read recognition results; referring, by the computer, to the recognition result and the acquired reliability to select an N-gram entry; and training, by the computer, the N-gram language model about selected one of more of the N-gram entries using all recognition results.

FOREIGN PRIORITY

This application claims priority to Japanese Patent Application No.2014-065470, filed Mar. 27, 2014, and all the benefits accruingtherefrom under 35 U.S.C. §119, the contents of which in its entiretyare herein incorporated by reference.

BACKGROUND

The present invention relates to a technique for improving anunsupervised training method for an N-gram language model.

Nowadays, language models are used in various fields such as speechrecognition, machine translation, and information retrieval. Thelanguage models are statistical models for assigning an occurrenceprobability to a string of words or a string of characters. The languagemodels include an N-gram model, a hidden Markov model, and a maximumentropy model, and among them, the N-gram model is most frequently used.

A language model is built by statistical training from training data.However, expressions used by users change from day to day, and newexpressions are unknown to the language model trained from old trainingdata. Therefore, the training data and the language model need to beupdated on a regular basis.

Manual updating of the training data and the language model is notrealistic. However, large amounts of speech data (hereinafter calledfield data) have recently been available with the emergence of a cloudtype speech recognition system or a server type speech recognitionsystem for providing voice search systems or speech recognitionservices, for example, in call centers. The results of unsupervisedautomatic recognition of these field data are useful to complementtraining data of the language model. The following will describeconventional techniques regarding the building of a language model andthe use of the results of automatic speech recognition to build thelanguage model or an acoustic model.

Norihiro Katsumaru, et al., “Language Model Adaptation for AutomaticSpeech Recognition to Support Note-Taking of Classroom Lectures,” TheSpecial Interest Group Technical Reports of IPSJ, SLP, Speech languageinformation processing, vol. 2008, no. 68, pp. 25-30, 2008 discloses atechnique for building a language model from utterance units including ahigh proportion of content words with the reliability of speechrecognition results higher than or equal to a threshold value whenuniversity lecture data are used for language model adaptation.

JP2011-75622 discloses a technique in which, when acoustic modeladaptation is performed using multiple adaptation data composed ofspeech data and text attached with a reliability obtained as a result ofspeech recognition of the speech data, unsupervised adaptation isperformed directly using adaptation data with a relatively highreliability, speech recognition text is manually correctedpreferentially for data having a phoneme environment that is notincluded in the adaptation data with the high reliability amongadaptation data with relatively low reliabilities to perform supervisedadaptation, and data with relatively low reliabilities and for whichtext is not corrected are applied with a weight lower than that of theother data to perform unsupervised adaptation.

JP2008-234657 discloses a technique as a pruning method for a languagemodel capable of pruning the language model in a size suitable for theapplication, in which all the highest order n-grams and theirprobabilities are removed from an n-gram language model M0 to generatean initial base model, and some of the most important pruned n-gramprobabilities are added to this initial base model to provide a prunedlanguage model.

Hui Jiang, “Confidence measures for speech recognition: A survey,”Speech Communication, Vol. 45, pp. 455-470, 2005 presents confidencemeasures available as three categories indicating the reliability ofrecognition results of automatic speech recognition. This literature iscited as a reference literature showing an example of reliabilityavailable in the present invention.

SUMMARY

In one embodiment, a computer-based, unsupervised training method for anN-gram language model includes reading, by a computer, recognitionresults obtained as a result of speech recognition of speech data;acquiring, by the computer, a reliability for each of the readrecognition results; referring, by the computer, to the recognitionresult and the acquired reliability to select an N-gram entry; andtraining, by the computer, the N-gram language model about selected oneof more of the N-gram entries using all recognition results.

In another embodiment, an unsupervised training system for an N-gramlanguage model includes a processing device configured to: readrecognition results obtained as a result of speech recognition of speechdata; acquire a reliability for each of the read recognition results;refer to the recognition result and the acquired reliability to selectan N-gram entry; and train the N-gram language model about selected oneof more of the N-gram entries using all recognition results.

In another embodiment, a non-transitory, computer readable storagemedium having instructions stored thereon that, when executed by acomputer, implement a training method for an N-gram language model. Themethod includes reading recognition results obtained as a result ofspeech recognition of speech data; acquiring a reliability for each ofthe read recognition results; referring to the recognition result andthe acquired reliability to select an N-gram entry; and training theN-gram language model about selected one of more of the N-gram entriesusing all recognition results.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of the hardware configuration of a computer 100suitable for implementing an unsupervised training system for an N-gramlanguage model according to an embodiment of the present invention.

FIG. 2A is a chart showing an outline of an example of the unsupervisedtraining system for the N-gram language model according to theembodiment of the present invention.

FIG. 2B is a chart showing an outline of another example of theunsupervised training system for the N-gram language model according tothe embodiment of the present invention.

FIG. 3 is a functional block diagram of an unsupervised training system300 for an N-gram language model according to the embodiment of thepresent invention.

FIG. 4 is a flowchart showing an example of a flow of unsupervisedtraining processing for the N-gram language model according to theembodiment of the present invention.

DETAILED DESCRIPTION

As shown in Katsumaru, et al., discussed above, only the parts in whichthe reliability of speech recognition results is higher than or equal tothe threshold value are used to build a language model so that use ofparts likely to be recognition errors can be avoided. However, when onlythe highly reliable parts are extracted to build the N-gram languagemodel, a distortion in the probabilities of N-gram entries to becalculated occurs. Further, as shown in Katsumaru, et al., even whenonly part of the data is targeted, it is not preferred to require humandetermination because it disturbs automation. Further, pruning of alanguage model shown in Jiang is a matter of choice of N-gram entries tobe left, and since a language model before pruning can be used as theprobabilities of N-gram entries, it cannot be applied to an unsupervisedtraining method for the N-gram language model.

The present invention has been made in view of the above problems in theconventional techniques, and it is an object thereof to provide animproved, unsupervised training method, training system, and trainingprogram for an N-gram language model, which neither requires any manualcorrection nor causes any distortion in the probabilities of N-gramentries.

In order to solve the above problems with the conventional techniques,embodiments of the present invention provide an unsupervised trainingmethod for an N-gram language model including the following features.The training method includes: causing a computer to read recognitionresults obtained as a result of speech recognition of speech data;causing the computer to acquire a reliability for each of the readrecognition results; causing the computer to refer to the recognitionresult and the acquired reliability to select an N-gram entry; andcausing the computer to train the N-gram language model about selectedone of more of the N-gram entries using all recognition results.

In one embodiment, the referring may include causing the computer toselect each of the N-gram entries, whose number of appearances in therecognition results with the reliability higher than or equal to apredetermined threshold value exceeds a predetermined number of times.

Instead of the above configuration, the referring may include causingthe computer to select each of the N-gram entries, whose number ofappearances in the recognition results exceeds a predetermined number oftimes, where the number of appearances is given a weight according tothe reliability.

Alternatively, the referring may include causing the computer to selecteach of the N-gram entries, whose sum of a first number of appearancesin a first corpus as a set of all the recognition results and a secondnumber of appearances in a second corpus as a subset of the recognitionresults with the reliability higher than or equal to a predeterminedthreshold value exceeds a predetermined number of times.

The referring may include causing the computer to select, from a firstcorpus, a second corpus, and a third corpus, each of the N-gram entries,whose sum of a first number of appearances in the first corpus as a setof all the recognition results, a second number of appearances in asecond corpus as a subset of the recognition results with thereliability higher than or equal to a predetermined threshold value, anda third number of appearances in the third corpus as a baseline of theN-gram language model exceeds a predetermined number of times, whereeach of the first number of appearances, the second number ofappearances, and the third number of appearances is given a differentweight, respectively.

Here, each of the weights respectively given to each of the first numberof appearances, the second number of appearances, and the third numberof appearances may be estimated in advance by an EM algorithm using alanguage model estimated from each of subsets of the first corpus, thesecond corpus, and the third corpus.

In addition, the training may include causing the computer to train theselected one or more N-gram entries using all the recognition resultsand adding, to a baseline N-gram language model, the one or more N-gramentries and corresponding probabilities of the N-gram entries obtainedas a result of the training.

The read recognition results of the speech data may be recognitionresults as a result of automatic speech recognition in a cloud typespeech recognition system or a server type speech recognition system.

The posterior probability of a text unit obtained upon speechrecognition of the speech data may be used as the reliability.

While embodiments of the present invention are described as theunsupervised training method for the N-gram language model, embodimentsof the present invention can also be understood as an unsupervisedtraining program for an N-gram language model causing a computer toexecute each step of such a training method, and an unsupervisedtraining system for an N-gram language model implemented by installingthe training program on the computer.

In embodiments of the present invention, an N-gram language model istrained by using recognition results obtained as a result of speechrecognition of speech data. According to the present invention, sinceall the recognition results are used to train selected N-gram entrieswhile referring to the reliabilities of the recognition results andselecting the N-gram entries from part of the recognition results, nodistortion in the probabilities of N-gram entries occur. The othereffects of the present invention will be understood from the descriptionof each embodiment.

While an embodiment for carrying out the present invention will bedescribed in detail below with reference to the accompanying drawings,the embodiment to be described below is not intended to limit theinventions according to the appended claims, and all the combinations ofthe features described in the embodiment are not necessarily essentialto the means for solving the problems. Note that the same referencenumerals are given to the same elements throughout the description ofthe embodiment.

FIG. 1 shows an exemplary hardware configuration of a computer 100 forcarrying out the present invention. In FIG. 1, an external storagedevice 114 or a ROM 106 can cooperate with an operating system to giveinstructions to a CPU 102 in order to record code of and various data onan unsupervised training program for an N-gram language model forcarrying out the present invention. Then, each of multiple computerprograms stored in the external storage device 114 or the ROM 106 isloaded into a RAM 104 and executed by the CPU 102. The external storagedevice 114 is connected to a bus 108 via a controller (not shown) suchas a SCSI controller. The computer program can be compressed or dividedinto multiple parts and recorded on multiple media.

The computer 100 also includes a display device 116 for presentingvisual data to a user. The display device 116 is connected to the bus108 via a graphics controller (not shown). The computer 100 can beconnected to a network through a communication interface 118 tocommunicate with another computer or the like.

The above-mentioned components are just an example, and all thecomponents are not necessarily essential components of the presentinvention. It goes without saying that the computer 100 for carrying outthe present invention can also include other components, such as aninput device like a keyboard and a mouse, and a speaker.

It will be readily understood from the foregoing that the computer 100is an information processing apparatus such as a normal personalcomputer, a workstation, or a mainframe computer, or a combinationthereof.

Here, a brief summary of a language model generated according to thepresent invention will first be described.

Since an N-gram language model generated in the present invention is asimple yet effective model, it is most frequently used in largevocabulary continuous speech recognition today. The occurrenceprobability of a string of words w₁ w₂ . . . w_(n) composed of n wordsin a language model P(W) can be expressed in the following equation:

$\begin{matrix}{{P\left( {w_{1},\ldots\mspace{14mu},w_{n}} \right)} = {\prod\limits_{i = 1}^{n}\;{P\left( w_{i} \middle| {w_{1}\mspace{14mu}\ldots\mspace{14mu} w_{i - 1}} \right)}}} & (1)\end{matrix}$

However, since it is generally difficult to estimate the probability ofthe equation (1), the N-gram language model is based on the assumptionthat the occurrence of a word depends only on the occurrence of theprevious (N−1) word(s). Therefore, the equation (1) can be approximatedas follows:

$\begin{matrix}{{P\left( {w_{1},\ldots\mspace{14mu},w_{n}} \right)} \approx {\prod\limits_{i = 1}^{n}\;{P\left( w_{i} \middle| {w_{i - N + 1}\mspace{14mu}\ldots\mspace{14mu} w_{i - 1}} \right)}}} & (2)\end{matrix}$

A model when N=1 is called a unigram, a model when N=2 is called bigram,and a model when N=3 is called a trigram. 2, 3, or 4 are frequently usedas N in speech recognition.

The conditional probability of the above mathematical expression (2) canbe determined by using maximum likelihood estimation from the numbers ofappearances of a string of N words and a string of (N−1) words appearingin a corpus. When the number of appearances of a string of wordsw_(i−N+1) . . . w_(i) is expressed by C(w_(i−N+1) . . . w_(i)), theconditional probability P(w_(i)|w_(i−N+1) . . . w_(i−1)) is expressed bythe following equation:

$\begin{matrix}{{P\left( w_{i} \middle| {w_{i - N + 1}\mspace{14mu}\ldots\mspace{14mu} w_{i - 1}} \right)} = \frac{C\left( {w_{i - N + 1}\mspace{14mu}\ldots\mspace{14mu} w_{i}} \right)}{C\left( {w_{i - N + 1}\mspace{14mu}\ldots\mspace{14mu} w_{i - 1}} \right)}} & (3)\end{matrix}$

However, in this calculation method, the occurrence probability of thestring of N words that happen not to appear in the corpus becomes zero.To prevent this, probability smoothing is performed. As a typicalsmoothing method for the N-gram probability, there is back-offsmoothing. This is a method of determining the occurrence probability ofthe string of N words that have not appeared from the occurrenceprobability of the string of (N−1) words. Note that the above method isjust one example, and any other method can be used to apply the presentinvention. In the present invention, unsupervised training is performedon such an N-gram language model by using the speech recognitionresults.

FIG. 2A is a chart showing an outline of an example of an unsupervisedtraining system for an N-gram language model according to the embodimentof the present invention. An unsupervised training system 200 for anN-gram language model shown in FIG. 2A selects an N-gram entry with ahigh appearance frequency from a highly reliable part of the recognitionresults of automatic speech recognition of speech data. On the otherhand, the training system 200 performs leaning of the N-gram languagemodel using all the recognition results in determining the probabilityof the N-gram entry. The selected N-gram entry and the probability ofthe N-gram entry may be used to build an N-gram language model fromscratch or to update an N-gram language model already built.

FIG. 2B is a chart showing an outline of another example of theunsupervised training system for the N-gram language model according tothe embodiment of the present invention. An unsupervised training system202 for an N-gram language model shown in FIG. 2B selects N-gram entriesrespectively with high appearance frequencies from a baseline corpus anda highly reliable part of the recognition results of automatic speechrecognition of speech data. Then, the training system 202 performstraining of the N-gram language model about the selected N-gram entriesby using the baseline corpus and the entire recognition results. Like inthe system 200 shown in FIG. 2A, the selected N-gram entries and theprobabilities of the N-gram entries obtained as a result of the trainingmay be used to build an N-gram language model from scratch or to updatean N-gram language model already built.

Thus, in the unsupervised training system for the N-gram language modelaccording to the embodiment of the present invention, the entirerecognition results including a low reliable part are used in trainingthe N-gram language model while selecting N-gram entry(entries) byreferring to highly reliable recognition results. This is because of thefollowing reason. Namely, since the highly reliable part is a subset ofthe entire recognition results thought the recognition results thereofare correct, it is a subset having a distribution different from theoriginal distribution of words. For example, suppose that the word“computer” is an easily-recognizable word. In this case, assuming thatthe 10,000 words are included in the entire field data and the word“computer” appears in the field data 100 times, the 1-gram probabilityis 0.01. However, if 1,000 words are selected as the highly reliablepart and all 100 “computer” words are included in the 1000 words becausethe word “computer” is easily recognizable, the 1-gram probability willbecome 0.1. To eliminate such a distortion, training of the N-gramlanguage model is performed using the entire recognition results in thepresent invention.

Referring to FIG. 3 and FIG. 4, an unsupervised training system 300 foran N-gram language model will be described in detail below. FIG. 3 is afunctional block diagram of the unsupervised training system 300 for theN-gram language model according to the embodiment of the presentinvention. FIG. 4 is a flowchart showing an example of a flow ofunsupervised training processing for the N-gram language model accordingto the embodiment of the present invention.

The unsupervised training system 300 for the N-gram language model shownin FIG. 3 includes a corpus A302, a corpus B304, a reliabilityacquisition section 306, and a language model training section 308. Thelanguage model training section 308 further includes a selection section310 and a probability calculation section 312.

The corpus A302 is a baseline corpus used to build a part that forms thebasis for the N-gram language model. As an example, the corpus A302 maybe a corpus having a domain and a style consistent with a targetapplication. As another example, the corpus A302 may be a corpus havingan open domain and an open style. Further, the corpus A302 may be acorpus intended for written words on a magazine, newspaper, or theInternet, or a corpus intended for spoken text data obtained bytranscribing a speech manually. In addition, the corpus A302 may be acorpus intended for both written words and spoken words.

The corpus B304 is a corpus composed of recognition results of automaticspeech recognition of speech data without manual intervention. Forexample, such recognition results can be acquired from a cloud typespeech recognition system or a server type speech recognition system forproviding voice search systems or speech recognition services, forexample, used in call centers. The recognition results can also beacquired by performing automatic speech recognition of speech data on TVnews or the Web in a speech recognition system prepared by itself. Itshould be noted that since speech recognition operates word by word, therecognition results can also be acquired in the form of being dividedinto words.

The reliability acquisition section 306 calculates by itself orexternally acquires a reliability indicating how reliable each text ofthe recognition results included in the corpus B304 is. A confidencemeasure to be newly derived in the future as well as currently knownconfidence measures (see Jiang) can be used as confidence measures toindicate the reliability to be acquired. Specifically, a confidencemeasure calculated as a logical sum of the likelihood of acoustic modeland the likelihood of language model, a confidence measure using theposterior probability of a text unit obtained upon speech recognition,and a confidence measure calculated by the recall or precision ofcorrect words as correct intersections between outputs of two or morespeech recognition systems. Here, the posterior probability of the textunit obtained upon speech recognition is used. The posterior probabilityof the text unit is calculated by taking the logarithm of the posteriorprobabilities obtained in units of phonemes or the like upon recognitionand adding them together.

If W denotes text of a recognition result and X denotes an observedspeech signal, the posterior probability of a document unit can beexpressed in the form of removing argmax from the following equation(5), where Σ denotes a set of all hypotheses:

$\begin{matrix}\begin{matrix}{\hat{W} = {\arg{\max\limits_{W \in \Sigma}{p\left( W \middle| X \right)}}}} \\{= {\arg{\max\limits_{W \in \Sigma}{\frac{{p\left( W \middle| X \right)} \cdot {p(W)}}{P(X)}(5)}}}}\end{matrix} & (4)\end{matrix}$

Although p(X) is not calculated when speech recognition is performed,p(X)-based normalization is performed when a reliability is calculated.P(X) is expressed by the following equation, where H denotes ahypothesis upon speech recognition:

$\begin{matrix}{{p(X)} = {{\sum\limits_{H}\;{p\left( {X,H} \right)}} = {\sum\limits_{H}\;{{p(H)} \cdot {p\left( X \middle| H \right)}}}}} & (6)\end{matrix}$

Since it is difficult to sum up all the hypotheses, there are proposedvarious methods of approximating P(X). For example, there is a method ofcalculating a hypothesis using a model (background model) matching withmany phonemes to calculate P(X).

The reliability calculated or acquired by the reliability acquisitionsection 306 is stored in the corpus B304 or another memory location inassociation with each text of the recognition results included in thesame corpus B304. Note that, although the reliability is calculated inthe text unit in the above description, the reliability can also becalculated in units of words. Further, when the speech recognitionresults are acquired externally from a cloud type speech recognitionsystem or a server type speech recognition system, the reliability orinformation necessary to calculate the reliability (for example, theposterior probability of the text unit obtained upon speech recognition)is also acquired.

The language model training section 308 uses both the corpus A302 andthe corpus B304 as training text to build an N-gram language model. Morespecifically, the language model training section 308 includes theselection section 310 for selecting N-gram entries that form an N-gramlanguage model, and the probability calculation section 312 fordetermining the probability of each of the selected N-gram entries. Notethat a subset of recognition results with corresponding reliabilitieshigher than or equal to a predetermined threshold value among therecognition results included in the corpus B304 is also called a corpusb below.

As an example, the selection section 310 selects, from the corpus A302and the corpus B304, each of N-gram entries, whose sum of the number ofappearances C_(A) in the corpus A302, the number of appearances C_(B) inthe corpus B304, and the number of appearances C_(b) in the corpus bexceeds a predetermined number of times. Here, the selection section 310gives a different weight to each of the number of appearances C_(A), thenumber of appearances C_(B), and the number of appearances C_(b) (w_(A),w_(B), and w_(b) in this order), respectively. Each of the weightsw_(A), w_(B), and w_(b) can be determined by estimating a language modelfrom each subset of the corpus A302, the corpus B304, and the corpus b,and performing optimization by an EM algorithm to maximize a generationprobability of text for development set in a target field by using theestimated language model. Alternatively, each of the weights w_(A),w_(B), and w_(b) may preset to a value derived from experience.

Since the corpus b is a subset of the corpus B304, a weight w_(B)+w_(b)is given to the number of appearances of each of N-gram entriesappearing in the corpus b. As a result, the N-gram entries appearing inthe corpus b are selected more positively than other N-gram entriesappearing only in the corpus B304.

Note that the use of the corpus A302 is optional, and N-gram entries maybe selected without using the corpus A302. In other words, the selectionsection 310 may refer to each of the recognition results included in thecorpus B304 and a corresponding reliability to select an N-gram entry.More specifically, the selection section 310 may select an N-gram entrywhose number of appearances in the corpus b exceeds the predeterminednumber of times.

Instead of the above configuration, the selection section 310 may give aweight corresponding to the reliability to the number of appearances ofeach N-gram entry appearing in the corpus B304 and count the number ofappearances. The selection section 310 may select each N-gram entrywhose number of appearances thus counted exceeds the predeterminednumber of times.

The selection section 310 may also select each of N-gram entries, whosesum of the number of appearances in the corpus B304 and the number ofappearances in the corpus b exceeds the predetermined number of times.

In any of the above configurations, the selection section 310 positivelyselects N-gram entries with high reliabilities from the recognitionresults.

The probability calculation section 312 uses all the recognition resultsincluded in the corpus A302 and the corpus B304 to train the N-gramlanguage model about one or more N-gram entries selected by theselection section 310, or when only the corpus B is used as trainingdata, the probability calculation section 312 uses all the recognitionresults included in the corpus B to train the N-gram language modelabout the selected one or more N-gram entries. The probabilitycalculation method is as described in the outline of the N-gram languagemodel. Further, a training tool, such as SRILM, the SRI languagemodeling toolkit from SRI International(http://www.speech.sri.com/projects/srilm/) or the like can be used.

The language model training section 308 may add, to the base N-gramlanguage model, the selected one or more N-gram entries and theirprobabilities obtained as a result of the training, or build the N-gramlanguage model from scratch.

Referring next to FIG. 4, the operation of the unsupervised trainingsystem for the N-gram language model according to the embodiment of thepresent invention will be described. Unsupervised training processingfor the N-gram language model is started at step 400. Then, thereliability acquisition section 306 reads recognition results from thecorpus B304 composed of the recognition results of automatic speechrecognition of speech data, and calculates by itself or externallyacquires the reliability of each of the recognition results (step 402).The acquired reliability is stored in association with the correspondingrecognition result.

Then, the selection section 310 of the language model training section308 selects N-gram entries that form at least part of the N-gramlanguage model from the corpus A302 as the baseline corpus and thecorpus B304 composed of the recognition results of automatic speechrecognition of speech data (step 404). Here, the selection section 310refers to the reliability calculated in step 402 to select each ofN-gram entries appearing in highly reliable recognition results morepositively than low reliable recognition results among the recognitionresults of automatic speech recognition of speech data.

Then, the probability calculation section 312 of the language modeltraining section 308 trains the N-gram language model about one or moreN-gram entries selected in step 404 using the corpus A302 and the corpusB304, i.e., using all the recognition results (step 406). After that,the processing is ended.

While the present invention has been described with reference to theembodiment, the technical scope of the present invention is not limitedto the description of the aforementioned embodiment. It will be obviousto those skilled in the art that various changes and modifications canbe added to the aforementioned embodiment. Therefore, forms to whichsuch changes or modifications are added shall be included in thetechnical scope of the present invention.

The operations, the procedure, the steps, and the execution sequence ofprocesses such as stages in the apparatus, system, program, and methoddescribed in the appended claims and the specification and shown in theaccompanying drawings are not particularly specified as “ahead of,”“prior to,” or the like. It should be noted that the operations and thelike can be carried out in any order unless the output of the previousprocess is used in the subsequent process. It should also be noted that,even when the output of the previous process is used in the subsequentprocess, any other process can intervene between the previous processand the subsequent process, or even when it is stated that any otherprocess intervenes, the order of operations can be altered to executethe previous process immediately before the subsequent process. In theappended claims, the specification, and the operation flow in thedrawings, “first,” “next,” “then,” and the like are used for conveniencesake, but it does not mean that it is imperative to carry out theoperations and the like in this order.

The invention claimed is:
 1. An unsupervised training system for anN-gram language model, comprising: a processor configured to: readrecognition results obtained as a result of speech recognition of speechdata; acquire a reliability for each of the read recognition results;refer to each recognition result's acquired reliability to select asubset of one or more N-gram entries based upon their respectivereliabilities; and train an N-gram language model for one of moreentries of the subset of N-gram entries using all recognition results,wherein the processing device is further configured to select from afirst corpus, a second corpus, and a third corpus, each of the N-gramentries, whose sum of a first number of appearances in the first corpusas a set of all the recognition results, a second number of appearancesin a second corpus as a subset of the recognition results with thereliability higher than or equal to a predetermined threshold value, anda third number of appearances in the third corpus as a baseline of theN-gram language model exceeds a predetermined number of times, whereeach of the first number of appearances, the second number ofappearances, and the third number of appearances is given a differentweight, respectively.
 2. The system of claim 1, wherein each of theweights respectively given to each of the first number of appearances,the second number of appearances, and the third number of appearances isestimated in advance by an EM algorithm using a language model estimatedfrom each of subsets of the first corpus, the second corpus, and thethird corpus.
 3. The system of claim 1, wherein the processing device isfurther configured to train the selected one or more N-gram entriesusing all the recognition results and adding, to a base N-gram languagemodel, the one or more N-gram entries and probabilities obtained as aresult of the training.
 4. The system of claim 1, wherein the acquiredrecognition results of the speech data are recognition results as aresult of automatic speech recognition in a cloud type speechrecognition system or a server type speech recognition system.
 5. Thesystem of claim 1, wherein a posterior probability of a text unitobtained upon speech recognition of the speech data is used as thereliability in the acquiring.
 6. A non-transitory, computer readablestorage medium having instructions stored thereon that, when executed bya computer, implement a training method for an N-gram language model,the method comprising: reading recognition results obtained as a resultof speech recognition of speech data; acquiring a reliability for eachof the read recognition results; referring to each recognition result'sacquired reliability to select a subset of one or more N-gram entriesbased upon their respective reliabilities; and training the N-gramlanguage model for one of more entries of the subset of N-gram entriesusing all recognition results, wherein the processing device is furtherconfigured to select each of the N-gram entries, whose sum of a firstnumber of appearances in a first corpus as a set of all the recognitionresults and a second number of appearances in a second corpus as asubset of the recognition results with the reliability higher than orequal to a predetermined threshold value exceeds a predetermined numberof times.
 7. The computer readable storage medium of claim 6, whereinthe method further comprises: training the selected one or more N-gramentries using all the recognition results and adding, to a base N-gramlanguage model, the one or more N-gram entries and probabilitiesobtained as a result of the training.
 8. The computer readable storagemedium of claim 6, wherein the acquired recognition results of thespeech data are recognition results as a result of automatic speechrecognition in a cloud type speech recognition system or a server typespeech recognition system.
 9. The computer readable storage medium ofclaim 6, wherein a posterior probability of a text unit obtained uponspeech recognition of the speech data is used as the reliability in theacquiring.
 10. An unsupervised training system for an N-gram languagemodel, comprising: a processor configured to: read recognition resultsobtained as a result of speech recognition of speech data; acquire areliability for each of the read recognition results; refer to eachrecognition result's acquired reliability to select a subset of one ormore N-gram entries based upon their respective reliabilities; and trainan N-gram language model for one of more entries of the subset of N-gramentries using all recognition results, wherein the processor is furtherconfigured to select each of the N-gram entries, whose sum of a firstnumber of appearances in a first corpus as a set of all the recognitionresults and a second number of appearances in a second corpus as asubset of the recognition results with the reliability higher than orequal to a predetermined threshold value exceeds a predetermined numberof times.
 11. The system of claim 10, wherein the processor is furtherconfigured to train the selected one or more N-gram entries using allthe recognition results and adding, to a base N-gram language model, theone or more N-gram entries and probabilities obtained as a result of thetraining.
 12. The system of claim 10, wherein the acquired recognitionresults of the speech data are recognition results as a result ofautomatic speech recognition in a cloud type speech recognition systemor a server type speech recognition system.
 13. The system of claim 10,wherein a posterior probability of a text unit obtained upon speechrecognition of the speech data is used as the reliability in theacquiring.